Grabbing SPINS gradients
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## v tibble 3.1.8 v dplyr 1.0.9
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
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## Loading required package: ExPosition
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## New names:
## Rows: 164640 Columns: 8
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): ROI, Network, Subject, Site dbl (4): ...1, grad1, grad2, grad3
## i Use `spec()` to retrieve the full column specification for this data. i
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## * `` -> `...1`
## [1] "record_id" "scanner"
## [3] "diagnostic_group" "demo_sex"
## [5] "demo_age_study_entry" "scog_rmet_total"
## [7] "scog_er40_total" "scog_tasit1_total"
## [9] "scog_tasit2_sinc" "scog_tasit2_simpsar"
## [11] "scog_tasit2_parsar" "scog_tasit3_lie"
## [13] "scog_tasit3_sar" "np_domain_tscore_process_speed"
## [15] "np_domain_tscore_att_vigilance" "np_domain_tscore_work_mem"
## [17] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [19] "np_domain_tscore_reasoning_ps"
## New names:
## Rows: 467 Columns: 43
## -- Column specification
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## (4): record_id, scanner, diagnostic_group, demo_sex dbl (36): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
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grad.sub <- spins_grads_wide$Subject[order(spins_grads_wide$Subject)]
behav.sub <- lol_spins_behav$record_id[order(lol_spins_behav$record_id)]
# behav.sub[behav.sub %in% grad.sub == FALSE]
# grad.sub[grad.sub %in% behav.sub == FALSE]
# complete.cases(spins_grads_wide)
# complete.cases(lol_spins_behav)
kept.sub <- lol_spins_behav$record_id[complete.cases(lol_spins_behav)==TRUE] # 420
## grab the matching data
behav.dat <- lol_spins_behav[kept.sub,c(6:19)]
spins_grads_wide_org <- spins_grads_wide[,-1]
rownames(spins_grads_wide_org) <- spins_grads_wide$Subject
grad.dat <- spins_grads_wide_org[kept.sub,]
## variables to regress out
regout.dat <- var2regout_num[kept.sub,]
behav_all <- lol_spins_behav[kept.sub,]
table_one <- CreateTableOne(vars = colnames(behav_all)[4:19], strata="diagnostic_group",data=behav_all)
lol_demo <-
read_csv('../data/spins_lolivers_subject_info_for_grads_2022-04-21(withcomposite).csv') %>%
filter(exclude_MRI==FALSE,
exclude_meanFD==FALSE,
exclude_earlyTerm==FALSE) %>% as.data.frame
## New names:
## Rows: 467 Columns: 46
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (39): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
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lol_demo$subject <- sub("SPN01_", "sub-", lol_demo$record_id) %>% sub("_", "", .)
rownames(lol_demo) <- lol_demo$record_id
lol_demo_match <- lol_demo[kept.sub,]
spins_demo <- lol_demo_match %>%
select(demo_sex, demo_age_study_entry, diagnostic_group, scog_rmet_total, scog_er40_total, #scog_mean_ea,
scog_tasit1_total,
scog_tasit2_total, scog_tasit3_total,np_composite_tscore, np_domain_tscore_att_vigilance,
np_domain_tscore_process_speed, np_domain_tscore_work_mem,
np_domain_tscore_verbal_learning, np_domain_tscore_visual_learning,
np_domain_tscore_reasoning_ps,
#bsfs_sec2_total, bsfs_sec3_total, bsfs_sec3_total, bsfs_sec4_total, bsfs_sec5_total, bsfs_sec6_total,
#fd_mean_rest
) %>% data.frame
colnames(spins_demo)
## [1] "demo_sex" "demo_age_study_entry"
## [3] "diagnostic_group" "scog_rmet_total"
## [5] "scog_er40_total" "scog_tasit1_total"
## [7] "scog_tasit2_total" "scog_tasit3_total"
## [9] "np_composite_tscore" "np_domain_tscore_att_vigilance"
## [11] "np_domain_tscore_process_speed" "np_domain_tscore_work_mem"
## [13] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [15] "np_domain_tscore_reasoning_ps"
rownames(spins_demo) <- lol_demo_match$subject
spins_demo %>%
group_by(diagnostic_group) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>% t
## [,1] [,2]
## diagnostic_group "case" "control"
## demo_age_study_entry "31.41532" "31.94767"
## scog_rmet_total "24.56855" "27.59649"
## scog_er40_total "31.83539" "33.54651"
## scog_tasit1_total "22.50000" "24.63953"
## scog_tasit2_total "47.52823" "54.47093"
## scog_tasit3_total "48.35102" "54.71512"
## np_composite_tscore "35.42387" "49.57059"
## np_domain_tscore_att_vigilance "39.49794" "47.64912"
## np_domain_tscore_process_speed "39.69355" "53.06395"
## np_domain_tscore_work_mem "41.27016" "49.15698"
## np_domain_tscore_verbal_learning "40.66532" "50.30233"
## np_domain_tscore_visual_learning "38.72984" "48.37791"
## np_domain_tscore_reasoning_ps "42.91129" "48.75581"
spins_demo %>%
group_by(diagnostic_group) %>%
summarize_if(is.numeric, sd, na.rm = TRUE) %>% t
## [,1] [,2]
## diagnostic_group "case" "control"
## demo_age_study_entry " 9.768209" "10.395267"
## scog_rmet_total "5.258051" "3.821886"
## scog_er40_total "4.549732" "3.319822"
## scog_tasit1_total "3.640750" "2.135267"
## scog_tasit2_total "8.526169" "4.228042"
## scog_tasit3_total "7.271608" "5.264379"
## np_composite_tscore "12.93041" "11.01147"
## np_domain_tscore_att_vigilance "11.65920" "12.71612"
## np_domain_tscore_process_speed "13.16429" "10.09612"
## np_domain_tscore_work_mem "11.19371" "11.36136"
## np_domain_tscore_verbal_learning "8.937756" "9.438716"
## np_domain_tscore_visual_learning "12.45736" "10.06134"
## np_domain_tscore_reasoning_ps "10.97108" " 9.54391"
cbind(table(spins_demo$diagnostic_group, spins_demo$demo_sex), table(spins_demo$diagnostic_group))
## female male
## case 79 169 248
## control 80 92 172
table(regout.dat$demo_sex_num)
##
## 0 1
## 159 261
behav.reg <- apply(behav.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg <- apply(grad.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg2plot <- apply(grad.dat, 2, function(x){
model <- lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)
return(model$residual + model$coefficient[1])
} )
networks <- read_delim("../networks.txt",
"\t", escape_double = FALSE, trim_ws = TRUE) %>%
select(NETWORK, NETWORKKEY, RED, GREEN, BLUE, ALPHA) %>%
distinct() %>%
add_row(NETWORK = "Subcortical", NETWORKKEY = 13, RED = 0, GREEN=0, BLUE=0, ALPHA=255) %>%
mutate(hex = rgb(RED, GREEN, BLUE, maxColorValue = 255)) %>%
arrange(NETWORKKEY)
## Rows: 718 Columns: 12
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (4): LABEL, HEMISPHERE, NETWORK, GLASSERLABELNAME
## dbl (8): INDEX, KEYVALUE, RED, GREEN, BLUE, ALPHA, NETWORKKEY, NETWORKSORTED...
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## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
networks$hex <- darken(networks$hex, 0.2)
# oi <- networks$hex
# swatchplot(
# "-40%" = lighten(oi, 0.4),
# "-20%" = lighten(oi, 0.2),
# " 0%" = oi,
# " 20%" = darken(oi, 0.2),
# " 25%" = darken(oi, 0.25),
# " 30%" = darken(oi, 0.3),
# " 35%" = darken(oi, 0.35),
# off = c(0, 0)
# )
# networks
pls.res <- tepPLS(behav.reg, grad.reg, center2 = FALSE, scale2 = FALSE, DESIGN = sub.dx$diagnostic_group, make_design_nominal = TRUE, graphs = FALSE)
pls.boot <- data4PCCAR::Boot4PLSC(behav.reg, grad.reg, scale1 = TRUE, center2 = FALSE, scale2 = FALSE, nIter = 1000, nf2keep = 4)
## Registered S3 method overwritten by 'data4PCCAR':
## method from
## print.str_colorsOfMusic PTCA4CATA
# ## swith direction for dimension 3
pls.res$TExPosition.Data$fi[,1] <- pls.res$TExPosition.Data$fi[,1]*-1
pls.res$TExPosition.Data$fj[,1] <- pls.res$TExPosition.Data$fj[,1]*-1
pls.res$TExPosition.Data$pdq$p[,1] <- pls.res$TExPosition.Data$pdq$p[,1]*-1
pls.res$TExPosition.Data$pdq$q[,1] <- pls.res$TExPosition.Data$pdq$q[,1]*-1
pls.res$TExPosition.Data$lx[,1] <- pls.res$TExPosition.Data$lx[,1]*-1
pls.res$TExPosition.Data$ly[,1] <- pls.res$TExPosition.Data$ly[,1]*-1
## Scree plot
PlotScree(pls.res$TExPosition.Data$eigs)
## Print singular values
pls.res$TExPosition.Data$pdq$Dv
## [1] 53.836609 15.734772 14.187177 11.529298 11.442135 10.418114 9.348353
## [8] 8.914484 8.304195 7.637942 7.131881 6.849054 6.265523 5.693384
## Print eigenvalues
pls.res$TExPosition.Data$eigs
## [1] 2898.38047 247.58304 201.27598 132.92471 130.92245 108.53709
## [7] 87.39170 79.46803 68.95966 58.33815 50.86372 46.90954
## [13] 39.25678 32.41463
pls.res$TExPosition.Data$t
## [1] 69.2857737 5.9184715 4.8115016 3.1775647 3.1297007 2.5945788
## [7] 2.0890982 1.8996828 1.6484804 1.3945734 1.2158971 1.1213724
## [13] 0.9384331 0.7748715
## Compare the inertia to the largest possible inertia
sum(cor(behav.dat, grad.dat)^2)
## [1] 81.59259
sum(cor(behav.dat, grad.dat)^2)/(ncol(behav.dat)*ncol(grad.dat))
## [1] 0.004955818
Here, we show that the effect that PLSC decomposes is pretty small to begin with. The effect size of the correlation between the two tables is 92.40 which accounts for 0.0065 of the largest possible effect.
lxly.out[[1]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,1],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,1] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.
cor.heat <- pls.res$TExPosition.Data$X %>% heatmap(col = col.heat)
## control
grad.dat.ctrl <- grad.dat[sub.dx$diagnostic_group == "control",]
behav.dat.ctrl <- behav.dat[sub.dx$diagnostic_group == "control",]
corX.ctrl <- cor(as.matrix(behav.dat.ctrl),as.matrix(grad.dat.ctrl))
heatmap(corX.ctrl[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
## case
grad.dat.case <- grad.dat[sub.dx$diagnostic_group == "case",]
behav.dat.case <- behav.dat[sub.dx$diagnostic_group == "case",]
corX.case <- cor(as.matrix(behav.dat.case),as.matrix(grad.dat.case))
heatmap(corX.case[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
lxly.out[[2]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,2],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,2] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim1.est <- pls.res$TExPosition.Data$pdq$Dv[1]*as.matrix(pls.res$TExPosition.Data$pdq$p[,1], ncol = 1) %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1], ncol = 1))
cor.heat.res1 <- (pls.res$TExPosition.Data$X - dim1.est) %>% heatmap(col = col.heat)
lxly.out[[3]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,3],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,3] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim2.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:2]) %*% pls.res$TExPosition.Data$pdq$Dd[1:2,1:2] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:2])))
cor.heat.res2 <- heatmap(pls.res$TExPosition.Data$X - dim2.est, col = col.heat)
lxly.out[[4]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,4],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,4] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim3.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:3]) %*% pls.res$TExPosition.Data$pdq$Dd[1:3,1:3] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:3])))
cor.heat.res3 <- heatmap(pls.res$TExPosition.Data$X - dim3.est, col = col.heat)
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## plot case
case_roi_results <- spins_mean_bygrp[,1:5] %>%
pivot_longer(grad1.case:grad3.case, names_to = "gradient", values_to = "grad_value") %>%
mutate(roi = str_remove(ROI, "_ROI"),
label = case_when(str_starts(ROI, "L") ~ str_c("lh_", roi),
str_starts(ROI, "R_") ~ str_c("rh_", roi))) %>%
filter(Network != "Subcortical") %>%
filter(ROI != "L_10pp_ROI") %>%
as.data.frame() %>%
group_by(gradient) %>%
ggplot() +
geom_brain(mapping = aes(fill = grad_value),
atlas = glasser) +
facet_wrap(~gradient, ncol = 1, labeller = labeller(gradient =
c("grad1.case" = "Gradient 1",
"grad2.case" = "Gradient 2",
"grad3.case" = "Gradient 3")
)) +
scale_fill_distiller(name = "Scores", palette = "BrBG", limits = c(-1.3,1.5), values = c(0, 0.286, 0.464, 0.643, 1)) +
ggtitle("SSD")+
theme(axis.text.y.left = element_blank(),
axis.text.x.bottom = element_blank()) +
theme_brain(text.family = "Calibri")
## plot control
control_roi_results <- spins_mean_bygrp[,c(1:2, 6:8)] %>%
pivot_longer(grad1.control:grad3.control, names_to = "gradient", values_to = "grad_value") %>%
mutate(roi = str_remove(ROI, "_ROI"),
label = case_when(str_starts(ROI, "L") ~ str_c("lh_", roi),
str_starts(ROI, "R_") ~ str_c("rh_", roi))) %>%
filter(Network != "Subcortical") %>%
filter(ROI != "L_10pp_ROI") %>%
as.data.frame() %>%
group_by(gradient) %>%
ggplot() +
geom_brain(mapping = aes(fill = grad_value),
atlas = glasser) +
facet_wrap(~gradient, ncol = 1, labeller = labeller(gradient =
c("grad1.control" = "Gradient 1",
"grad2.control" = "Gradient 2",
"grad3.control" = "Gradient 3")
)) +
scale_fill_distiller(name = "Scores", palette = "BrBG", limits = c(-1.3,1.5), values = c(0, 0.286, 0.464, 0.643, 1)) +
ggtitle("Controls")+
theme(axis.text.y.left = element_blank(),
axis.text.x.bottom = element_blank()) +
theme_brain(text.family = "Calibri")
## compute the difference with GLM
all_roi_results <- spins_grad_everything %>%
pivot_longer(grad1:grad3, names_to = "gradient", values_to = "grad_value") %>%
ungroup() %>%
group_by(Network, gradient, ROI) %>%
do(tidy(lm(grad_value ~ diagnostic_group, data = .))) %>%
ungroup() %>%
group_by(term) %>%
mutate(p_FDR = p.adjust(p.value, method = "fdr"))
## plot the F statistics
all_roi_results %>%
filter(term == "diagnostic_groupcontrol") %>%
mutate(roi = str_remove(ROI, "_ROI"),
label = case_when(str_starts(ROI, "L") ~ str_c("lh_", roi),
str_starts(ROI, "R_") ~ str_c("rh_", roi))) %>%
filter(Network != "Subcortical") %>%
filter(ROI != "L_10pp_ROI") %>%
as.data.frame() %>%
group_by(gradient) %>%
ggplot() +
geom_brain(mapping = aes(fill = statistic),
atlas = glasser) +
facet_wrap(~gradient, ncol = 1, labeller = labeller(gradient =
c("grad1" = "Gradient 1",
"grad2" = "Gradient 2",
"grad3" = "Gradient 3")
)) +
scale_fill_distiller(name = "F value", palette = "RdBu", limits = c(-7,7), values = c(0, 0.25, 0.5, 0.75, 1)) +
ggtitle("Gradients: Controls > SSD")+
theme(axis.text.y.left = element_blank(),
axis.text.x.bottom = element_blank()) +
theme_brain(text.family = "Calibri")
## merging atlas and data by 'label'
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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3D plot of the gradients
We need to interpret the arrows with cautious, because only the direction and the magnitude are meaningful but not the end point.
We need to interpret the arrows with cautious, because only the direction and the magnitude are meaningful but not the end point.
We need to interpret the arrows with cautious, because only the direction and the magnitude are meaningful but not the end point.
## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## EOF within quoted string